THE RISING COST OFONSHORE WIND

Andrew Montford

The Global Warming Policy Foundation
Briefing 59

The Rising Cost of Onshore Wind
Andrew Montford
Briefing 59, The Global Warming Policy Foundation© Copyright 2021, The Global Warming Policy Foundation

Contents
About the author	iii

1. Introduction	

1

2. Capital costs	2

3. Operational costs	

4

4. Financing costs	

5

5. Capacity factors	

6

6. Levelised cost	9

7. Conclusions	

11

Notes	12

About the Global Warming Policy Foundation	

14

About the author
Andrew Montford is deputy director of the Global Warming Policy Foundation.

iii

1. Introduction
My recent paper on offshore wind1 set out the extraordinary
discrepancy between the levelised cost of electricity (LCOE) as
revealed by accounts data and the claims made by advocates ofgreen energy, including official bodies like BEIS and the Committee on Climate Change. This paper will look at estimates of the
LCOE of onshore wind.
As in the earlier paper, I will examine claims made by the
Department for Business, Energy and Industrial Strategy (BEIS),2
Bloomberg New Energy Finance (BNEF),3 the International Renewable Energy Agency (IRENA),4 and the merchant bank, Lazard.5 As in the previous paper, I will refer to these organisations
collectively as ‘the advocates’.
I will compare the advocates’ claims to my own estimates of
LCOE, based on Ofgem’s generation figures and data publishedin audited financial accounts, and the recent comprehensive review of UK windfarm financial accounts by Hughes.6 My sample
includes all UK onshore windfarms larger than 25 MW for which
accounts are available, approximately 40% of windfarms of thatsize.7
I will look at disaggregated capital and operational expenditure estimates, capacity factors (a measure of what proportionof maximum capacity that is delivered each year), as well as thelevelised cost, which brings these parameters (and others) together. For each component, I will look at different views on thehistory, the current position, and the predictions that are being
made about its future evolution.
It is worth repeating my earlier caveats. LCOE has beenstrongly criticised when applied to intermittent energy generators such as offshore wind, because it presents a misleadingly
optimistic view.8 Nevertheless, renewables advocates and the
media continue to use it as a way to make claims about the viability of such technologies. However, this paper will show thatthe claims of renewables advocate are unfounded, even on an
LCOE basis.

12. Capital costs
Figure 1 shows views on the history of onshore windfarm capitalcosts per megawatt of capacity. The blue dots represent data extracted from the financial accounts of large UK onshore windfarms.9 The
upward trend since the start of the century is clear. It is notable that
Hughes’ dataset, which covers windfarms of all sizes, finds a very similar pattern. The two lines represent the positions of IRENA and BNEF.10
Both show a remarkably different pattern. This may partly be due tothe length of the two series, going back to the very earliest days of
the wind power industry, when small experimental facilities were thenorm. Nevertheless, the failure of both organisations to detect thesteady increase in the cost of a megawatt of wind turbine capacitythat is shown in both the GWPF and Hughes datasets, both of which
are based on audited financial statements, is surprising. One possible
explanation is that the data for both of the advocates’ graphs is for a
global average, whereas the GWPF figures are only for the UK. Another possibility is that, while the GWPF figures are only for larger windfarms, the advocates do not mention any restriction on size. Thus the

Capital cost (£m/MW capacity)

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2

1

0

3

2

1

1989
1989

1999

2009

1999

2009

BNEF (global)

IRENA

Figure 1: Capital costs: views on the history
2

2019
2019

GWPF

0

apparent decline represented in the advocates’ graphs may merelyreflect a greater preponderance of large windfarms rather than anunderlying fall in costs. Nevertheless, it is noteworthy that both of the
advocates find little change in the capital costs of onshore windfarms
in the last twenty years.
But what about predictions of future capital costs? Figure 2
shows the GWPF data again for reference, and then adds advocates’
views on how they expect those costs to change in future. The predictions include those from BEIS and Lazard. Note that the Lazard data is
for the USA, but the others, including BNEF, are for the UK alone. It isclear that all of the advocates believe that the 20-year upward trend
in capital costs reversed last year. They are expecting the average
cost for windfarms commissioning in the next few years to be morethan 40% below the average for the last five years. The disconnect
between past and future is striking, and makes their predictions lessthan convincing.

Capital cost (£m/MW capacity)

3

2

1

2010

0
2000

2020

2030

2010

GWPF

2020

Lazard

BNEF

2030

National Grid

Figure 2: Capital costs: predictions
3

2040

BEIS (range)

3. Operational costs
Figure 3 shows the moving average operational costs per
megawatt of capacity, for larger windfarms in the UK, by yearof operation. More recent windfarms are readily identifiable
as the shorter data series. It is clear at a glance that the operational costs for any given windfarm increase year by year as
the turbines age. It also seems that the cost per megawatt ofcapacity is higher for more recent windfarms.
We can extrapolate the trends in these data to estimate
an average opex per megawatt hour over the 20-year lifespan of the the windfarm. Figure 4 shows the results, plottedby commission date. There is clearly an upward trend overtime. In other words windfarm opex is becoming more expensive, even after correcting for inflation. This observationis consistent with Hughes, who notes that the trend is still
upwards – at a remarkable rate of 2.8% per year – even after
correcting for turbine size.
However, there is an interesting contrast with IRENA,
who say they can detect a steady decline in opex costs inwindfarms in a selection of EU countries (excluding the UK)
and the USA. The range of these estimates is indicated by theyellow bars in Figure 4.

120

Operating cost (£000/MW capacity)

100
80
60
40
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0
1

3

5

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9
Year of operation

11

Figure 3: Operating costs: history

13

15

Three-year moving averages from accounts data. For clarity, the data shown are a subsample of the full GWPF dataset,covering only windfarms greater than 40 MW capacity.

4

120

2000

Operating cost (£000/MW capacity)

100
80
60
40
20

2010

0
2000

2020

2030

2010

GWPF

Lazard (USA)2020
IRENA (5EU, US)

2030

BNEF

National Grid

2040
BEIS

Figure 4: Operating costs: history and predictions

GWPF figures are estimated average annual opex per megawatt over a 20-year liftime. The IRENA figures and thepredictions are assumed to be on the same basis.

Figure 4 also shows the opex predictions made by the
advocates. The disconnect between the historical data andthe claims about the future is again extraordinary, with noneof them appearing plausible given the history. It is possiblethat some of the advocates’ figures actually represent firstyear figures. If so, a 5% annual growth rate might lead to a
lifetime average that was 60% higher. This would make thefigures more plausible, but still very low.

4. Financing costs
It is generally agreed that financing costs have fallen, as investors have decided (rightly or wrongly) that the risks associated with windfarms have fallen.

2040

2010

2045

2015 205020202055 2025
5

2030

2035

2040

204

5. Capacity factors
No electricity generator produces 100% of its capacity over the courseof a year. Even gas-fired power stations have to shut down for maintenance, although they can still deliver over 90% of that theoretical maximum over a year, if given the chance. The percentage of the maximumthat a generator delivers is known as the capacity factor or load factor.
In the UK, the data in this area is excellent: it is possible to calculate capacity factors for all grid-connected windfarms from data in a publicallyaccessible database.11
Renewables deliver much lower capacity factors than fossil fuels. As
they age, their output deteriorates, as can be seen in Figure 5, although
for some windfarms there is a period in the early years in which operators seem to optimise the performance of their turbines, and capacity
factors increase. Most large onshore windfarms never achieve a capacity factor above 30%, and those that that do, only do so for a few years.
The two advocates that do portray the history of onshore windcapacity factors are IRENA and BNEF. Their data are shown in Figure 6,
although note that BNEF’s figures are global, not just for the UK. Bothorganisations discern a steady improvement, with figures of 35–40%
achieved in recent years. This rise suggests that the graphs represent
first-year (or early-years) performance rather than a fleet average, inwhich the poorer performance of older windfarms would tend to reduce the average.12 The figure also shows recent performance of thewhole UK onshore windfleet. The dark blue line is the average achievedby new large windfarms each year, where ‘new’ is defined as windfarms
commissioned two years earlier (so as to allow time for teething problems to be resolved). The other two lines represent the capacity factors
40

Capacity factor (%)

30

20

10

0
1

3

5

7
9
Year of operation

11

Figure 5: Capacity factors: deterioration over time

13

15

The graph shows all windfarms with three or more years of data available, presented as a three-year moving average.
Source: Ofgem REGO data.

6

Capacity factor (%)

60

40

20

0
2000

2005
UK fleet analysis
New windfarms >25 MW
All windfarms >25 MW
All windfarms

2010

IRENA

2015

2020

BNEF (global)

Figure 6: Capacity factors: history

UK fleet analysis data from Ofgem REGO/RO database. New windfarms are those commissioned two years before theyear for which data is reported.

for all large windfarms and for the whole fleet. All three suggest that any
improvement in output has been small at best, and possibly even nonexistent.
It is possible that the gap that has opened up between the newlarge windfarms and the fleet average represents a step change in performance. To test this assumption it is possible to treat each year’s newly
commissioned windfarms as a cohort and follow their capacity factors ineach year that follows.13 We would expect, by year 3, any teething problems to have been resolved and that the capacity factor would be well
above the fleet average, and this is indeed what we see: on average, thecohort will 16% higher than the large-windfarm average. But by year 6,
this advantage has fallen to zero.
The high values claimed by IRENA and BNEF are something of a mystery. While there are undoubtedly windfarms with capacity factors over
35%, they are the exception rather than the rule. BNEF’s figures are global, and will thus take in countries such as the USA, where larger turbinesand less undulating terrain give better performance. However, there arefew places in the world with such good wind resources as the UK. IRENA,
meanwhile, does not maintain comprehensive records of renewable assets, since they cover all technologies in all countries, so it is possible thatthey have hit on some of the outliers on the high side in the UK. Eitherway, neither of these two graphs should be considered representative ofthe true performance of UK onshore windfarms.
7

Few of the advocates make predictions made about future performance of onshore wind turbines, and only one is UK-specific. However,there is again an extraordinary disconnect between the history and theclaims made (Figure 7).
The blue line is the record of capacity factors for new large windfarms in the UK, taken from Figure 6. The grey line represents Lazard,
whose position – capacity factors of 38–55% – is again for US windfarms only.14 However, the US Energy Information Administration, thebody officially charged with monitoring such figures, takes a position
very similar to BNEF and IRENA, with capacity factors rising from 29%
in 2010 to nearly 34% in 2019.15 The Lazard figures are clearly absurd.
The red line is the prediction of BEIS, who are suggesting an equally implausible figure of 34%, although they see no improvement in thatfigure in coming decades. That value might represent the performanceof the best windfarms in their early years, but as such it is an outlier, andnot the basis for policy decisions.
Finally, the green line and shaded range is BNEF’s longer term prediction and upper and lower bounds for onshore wind turbine performance.16 However, they say these figures are for China, which might becompared to the USA, with lower wind speeds than the UK,17 but withless turbulent wind. Note also that a small adjustment has been madeto the figures to make them comparable to the others.18 The steady risein capacity factors that BNEF are predicting for China seems implausible given the history for the UK.

Capacity factor (%)

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2000

40

20

0

2010

2020
2010

2030
2020
2030
Year of commission

BNEF (China)

Lazard (USA)

2040

2050

BEIS

Figure 7: Capacity factors: the disconnect between history and predictions

The GWPF estimate is fleet average for large UK windfarms based on audited accounts as per Figure 6.

8

6. Levelised cost
Figure 8 shows the views on the history of levelised cost
as reported by IRENA and the current value according to
BNEF.
IRENA’s take is surprising; the graph in the original report extends back to 1989, thus preceding the commissioning of the UK’s first commercial onshore windfarm by
two years.
IRENA detects a strong decline in the overall cost of
onshore wind in the UK, which matches a similar decline
found by BNEF for the globe and by Lazard for the US (notshown). The scale of the claimed cost reduction for all
three since 2000 is remarkable – over 50% in each case.
The contrast with the GWPF sample is again remarkable, and it is worth taking a moment to consider the impact of the cost drivers considered in earlier sections onlevelised cost. For example, both BNEF and IRENA believethat there has been only a slight fall in capital costs in thelast two decades (Figure 1), but rise in capacity factors ofperhaps 15% or more (Figure 6), although BNEF’s figures
are global rather than UK-only. These increases cannot, on
their own, explain the fall in LCOE that the advocates claim
has taken place.

160

Levelised cost (£/MWh)

120

80

40

2000

2010
0

1990

2020
1995

Windfarms >25 MW

2030

2000

2005

IRENA

2010

2015

BNEF

Figure 8: Levelised cost: views on the history

The high value for the GWPF sample in 2011 is because there is only one data point behind the value for that year, and it
was an outlier.

9IRENA believes there has been a fall in operating costsin the US and in a selection of EU countries, although this isclearly not the case for large windfarms in the UK (Figure 4).
In fact, in the GWPF sample, capex represents a relatively
stable 60% of the LCOE over the years, as might be expectedfrom rising capex, rising opex, and stable capacity factor.
The advocates’ position of falling costs seems to rely on a fallin opex which does not show up in the GWPF sample, and astrong rise in capacity factors which appears to be spurious,or at least not applicable to the UK.
There are also few predictions of levelised cost, with
only BEIS and BNEF doing so for the UK. These are shown in
Figure 9. There is a curious disconnect between the values
given by BNEF for current windfarms and the start point of
its predictions, but it is obvious that the prediction of bothorganisations are entirely divorced from the history, as recorded in the GWPF record.

120

Levelised cost (£/MWh)

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80
60
40
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0
2000

2010

GWPF sample

2020

2030

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2020

BNEF (prediction)

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BNEF (current)

Figure 9: Levelised cost: predictions

2040

BEIS

The high value for the GWPF sample in 2011 is because there is only one data point behind the value for that year, and it
was an outlier.

107. Conclusions
As this analysis makes clear, real-world data – from audited
accounts and official power generation figures – show thatthe levelised cost of major onshore windfarms in the UK isrising rather than falling. At over £80/MWh, it is perhaps
twice the cost of electricity from gas turbines running flat
out,19 even before considering the considerable extra burden of dealing with intermittency. In the midst of a economic crisis of historic magnitude, it is surprising that anyone
would seek to expand its use. Yet that is what politicians of
all stripes seek to do.
In the companion paper to this one, on offshore windfarms, I concluded that the levelised cost claims of renewables advocates bore little resemblance to the recent historyof the different cost drivers involved. As this paper shows,their claims about onshore wind are, if anything, even lessplausible. That steady increases in costs should, over a year
or two, suddenly change into precipitous falls would be extraordinary if it came to pass.
But extraordinary claims demand extraordinary evidencel as yet we only have lines on graphs. In order for the
advocates’ claims to hold water, they need to explain which
windfarms they believe are operating at the low cost levels
they say are now the norm. My plea to the advocates is tomake public the data to support their claims.

11Notes

1. Montford A. Offshore wind: Cost predictions and cost outcomes. Briefing 52, The Global Warming
Policy Foundation, 2021.
2. Electricity Generation Costs 2020. Technical report, Department for Business, Energy and Industrial Strategy, 2020.
3. Brandily T. 2H 2019 LCOE Update: Solar, wind and power prices at the crossroads. Bloomberg New
Energy Finance, 2019.
4. IRENA. Renewable Power Generation Costs in 2019. Technical report, International Renewable
Energy Agency, 2020.
5. Lazard. Levelized Cost of Energy and Levelized Cost of Storage 2020. https://www.lazard.com/perspective/lcoe2020.
6. Hughes G. Wind Power Economics: Rhetoric and & Reality. Volume I, Wind Power Costs in the
United Kingdom. Technical report, Renewable Energy Foundation, 2020.
7. Financial records for windfarms operated by the large operators are sometimes subsumed intoa larger corporate entity.
8. See, for example, Joskow PL Comparing the costs of intermittent and dispatchable electricity
generating technologies. Discussion paper. https://economics.mit.edu/files/6317.
9. The dataset covers all windfarms greater than 25 MW, where there is a special purpose vehicle – in other words a set of financial records covering that one windfarm. In addition, one or two
windfarms that have added extra turbines part-way through the windfarm’s life have been excluded.
10. The figures are derived from p. 97 of the BNEF paper, with dollar figures converted to Sterlingat an average rate for the year in question.
11. https://renewablesandchp.ofgem.gov.uk. The database lists the capacity of each windfarm,the number of certificates issued in any given period, and the number of certificates per megawatt
hour to which the windfarm is eligible. It is thus trivial to calculate the number of megawatt hours
generated and the capacity factor.
12. The sample of windfarms in Figure 6 is larger than what is shown in Figure 5, the latter being
restricted to windfarms with at least three years of records.
13. This was tested by following the performance of the 2011, 2012, 2013, 2014 and 2015 cohorts
through to 2019–20. The cohort covered only windfarms in the GWPF sample, i.e. with capacity> 25 MW.
14. This can easily be determined by looking at the Global Wind Atlas at https://globalwindatlas.
info/, and comparing the US to somewhere like the Southern Uplands of the UK. On hilltops in theregion, for example where the Clyde windfarm is situated, wind power may be as high as 1530  W/
m2. It is hard to find anywhere with that sort of power in the USA.
15. EIA. Electric Power Monthly, Table 6.07.B. Capacity Factors for Utility Scale Generators Primarily Using Non-Fossil Fuels. See https://www.eia.gov/electricity/monthly/epm_table_grapher.
php?t=epmt_6_07_b.
16. See p. 97.
17. Global Wind Atlas, op. cit.
18. The graph notes that the figures are gross capacity factors; in other words, reducing the theoretical capacity only to take account of wind availability. However, other factors, such as transmission losses, repair time, turbulence, icing of the blades, and so on, reduce the actual yield of electricity still further. A conversion factor of 86% has been used to estimate net capacity factors forthe purposes of Figure 6. This figure is derived from http://www.windaction.org/posts/3528-clipper-windpower-the-economics-of-wind-energy.
19. The current spike in gas prices is mostly irrelevant to estimates of LCOE, which must be basedon an estimate of lifetime average fuel costs.

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